{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca160249",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 1 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 0 1 1 1]\n",
      " [1 1 1 0 0 1 1 1]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 1 代的最优利润: 2526.10\n",
      "第 1 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 2 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 0 1 1 1]\n",
      " [1 1 1 0 0 1 1 1]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 2 代的最优利润: 2526.10\n",
      "第 2 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 3 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 0 1 1 1]\n",
      " [1 1 1 0 0 1 1 1]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 3 代的最优利润: 2526.10\n",
      "第 3 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 4 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 0 1 1 1]\n",
      " [1 1 1 0 0 1 1 1]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 4 代的最优利润: 2526.10\n",
      "第 4 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 5 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 0 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 5 代的最优利润: 2346.60\n",
      "第 5 代的最优销量: 74.58\n",
      "--------------------------------------------------\n",
      "第 6 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 0 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 6 代的最优利润: 2346.60\n",
      "第 6 代的最优销量: 74.58\n",
      "--------------------------------------------------\n",
      "第 7 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 0 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 7 代的最优利润: 2346.60\n",
      "第 7 代的最优销量: 74.58\n",
      "--------------------------------------------------\n",
      "第 8 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 0 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 8 代的最优利润: 2346.60\n",
      "第 8 代的最优销量: 74.58\n",
      "--------------------------------------------------\n",
      "第 9 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 9 代的最优利润: 2526.50\n",
      "第 9 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 10 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 10 代的最优利润: 2526.50\n",
      "第 10 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 11 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 11 代的最优利润: 2526.50\n",
      "第 11 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 12 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 12 代的最优利润: 2526.50\n",
      "第 12 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 13 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 13 代的最优利润: 2526.50\n",
      "第 13 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 14 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 14 代的最优利润: 2526.50\n",
      "第 14 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 15 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 15 代的最优利润: 2526.50\n",
      "第 15 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 16 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 16 代的最优利润: 2526.50\n",
      "第 16 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 17 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 17 代的最优利润: 2526.50\n",
      "第 17 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 18 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 18 代的最优利润: 2526.50\n",
      "第 18 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 19 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 19 代的最优利润: 2526.50\n",
      "第 19 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 20 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 20 代的最优利润: 2526.50\n",
      "第 20 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 21 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 21 代的最优利润: 2526.50\n",
      "第 21 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 22 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 22 代的最优利润: 2526.50\n",
      "第 22 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 23 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 23 代的最优利润: 2526.50\n",
      "第 23 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 24 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 24 代的最优利润: 2526.50\n",
      "第 24 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 25 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 25 代的最优利润: 2526.50\n",
      "第 25 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 26 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 26 代的最优利润: 2526.50\n",
      "第 26 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 27 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 27 代的最优利润: 2526.50\n",
      "第 27 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 28 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 28 代的最优利润: 2526.50\n",
      "第 28 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 29 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 29 代的最优利润: 2526.50\n",
      "第 29 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 30 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 30 代的最优利润: 2526.50\n",
      "第 30 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 31 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 31 代的最优利润: 2526.50\n",
      "第 31 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 32 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 32 代的最优利润: 2526.50\n",
      "第 32 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 33 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 33 代的最优利润: 2526.50\n",
      "第 33 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 34 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 34 代的最优利润: 2526.50\n",
      "第 34 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 35 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 35 代的最优利润: 2526.50\n",
      "第 35 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 36 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 36 代的最优利润: 2526.50\n",
      "第 36 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 37 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 37 代的最优利润: 2526.50\n",
      "第 37 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 38 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 38 代的最优利润: 2526.50\n",
      "第 38 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 39 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 39 代的最优利润: 2526.50\n",
      "第 39 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 40 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 40 代的最优利润: 2526.50\n",
      "第 40 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 41 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 41 代的最优利润: 2526.50\n",
      "第 41 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 42 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 42 代的最优利润: 2526.50\n",
      "第 42 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 43 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 43 代的最优利润: 2526.50\n",
      "第 43 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 44 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 44 代的最优利润: 2526.50\n",
      "第 44 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 45 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 45 代的最优利润: 2526.50\n",
      "第 45 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 46 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 46 代的最优利润: 2526.50\n",
      "第 46 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 47 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 47 代的最优利润: 2526.50\n",
      "第 47 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 48 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 48 代的最优利润: 2526.50\n",
      "第 48 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 49 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 49 代的最优利润: 2526.50\n",
      "第 49 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 50 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 50 代的最优利润: 2526.50\n",
      "第 50 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 51 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 51 代的最优利润: 2526.50\n",
      "第 51 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 52 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 52 代的最优利润: 2526.50\n",
      "第 52 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 53 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 53 代的最优利润: 2526.50\n",
      "第 53 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 54 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 54 代的最优利润: 2526.50\n",
      "第 54 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 55 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 55 代的最优利润: 2526.50\n",
      "第 55 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 56 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 56 代的最优利润: 2526.50\n",
      "第 56 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 57 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 57 代的最优利润: 2526.50\n",
      "第 57 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 58 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 58 代的最优利润: 2526.50\n",
      "第 58 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 59 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 59 代的最优利润: 2526.50\n",
      "第 59 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 60 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 60 代的最优利润: 2526.50\n",
      "第 60 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 61 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 61 代的最优利润: 2526.50\n",
      "第 61 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 62 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 62 代的最优利润: 2526.50\n",
      "第 62 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 63 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 63 代的最优利润: 2526.50\n",
      "第 63 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 64 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 64 代的最优利润: 2526.50\n",
      "第 64 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 65 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 65 代的最优利润: 2526.50\n",
      "第 65 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 66 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 66 代的最优利润: 2526.50\n",
      "第 66 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 67 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 67 代的最优利润: 2526.50\n",
      "第 67 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 68 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 68 代的最优利润: 2526.50\n",
      "第 68 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 69 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 69 代的最优利润: 2526.50\n",
      "第 69 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 70 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 70 代的最优利润: 2526.50\n",
      "第 70 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 71 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 71 代的最优利润: 2526.50\n",
      "第 71 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 72 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 72 代的最优利润: 2526.50\n",
      "第 72 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 73 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 73 代的最优利润: 2526.50\n",
      "第 73 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 74 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 74 代的最优利润: 2526.50\n",
      "第 74 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 75 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 75 代的最优利润: 2526.50\n",
      "第 75 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 76 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 76 代的最优利润: 2526.50\n",
      "第 76 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 77 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 77 代的最优利润: 2526.50\n",
      "第 77 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 78 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 78 代的最优利润: 2526.50\n",
      "第 78 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 79 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 79 代的最优利润: 2526.50\n",
      "第 79 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 80 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 80 代的最优利润: 2526.50\n",
      "第 80 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 81 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 81 代的最优利润: 2526.50\n",
      "第 81 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 82 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 82 代的最优利润: 2526.50\n",
      "第 82 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 83 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 83 代的最优利润: 2526.50\n",
      "第 83 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 84 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 84 代的最优利润: 2526.50\n",
      "第 84 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 85 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 85 代的最优利润: 2526.50\n",
      "第 85 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 86 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 86 代的最优利润: 2526.50\n",
      "第 86 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 87 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 87 代的最优利润: 2526.50\n",
      "第 87 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 88 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 88 代的最优利润: 2526.50\n",
      "第 88 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 89 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 89 代的最优利润: 2526.50\n",
      "第 89 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 90 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 90 代的最优利润: 2526.50\n",
      "第 90 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 91 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 91 代的最优利润: 2526.50\n",
      "第 91 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 92 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 92 代的最优利润: 2526.50\n",
      "第 92 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 93 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 93 代的最优利润: 2526.50\n",
      "第 93 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 94 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 94 代的最优利润: 2526.50\n",
      "第 94 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 95 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 95 代的最优利润: 2526.50\n",
      "第 95 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 96 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 96 代的最优利润: 2526.50\n",
      "第 96 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 97 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 97 代的最优利润: 2526.50\n",
      "第 97 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 98 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 98 代的最优利润: 2526.50\n",
      "第 98 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 99 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 99 代的最优利润: 2526.50\n",
      "第 99 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 100 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 100 代的最优利润: 2526.50\n",
      "第 100 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 101 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 101 代的最优利润: 2526.50\n",
      "第 101 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 102 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 102 代的最优利润: 2526.50\n",
      "第 102 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 103 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 103 代的最优利润: 2526.50\n",
      "第 103 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 104 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 104 代的最优利润: 2526.50\n",
      "第 104 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 105 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 105 代的最优利润: 2526.50\n",
      "第 105 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 106 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 106 代的最优利润: 2526.50\n",
      "第 106 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 107 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 107 代的最优利润: 2526.50\n",
      "第 107 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 108 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 108 代的最优利润: 2526.50\n",
      "第 108 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 109 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 109 代的最优利润: 2526.50\n",
      "第 109 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 110 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 110 代的最优利润: 2526.50\n",
      "第 110 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 111 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 111 代的最优利润: 2526.50\n",
      "第 111 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 112 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 112 代的最优利润: 2526.50\n",
      "第 112 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 113 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 113 代的最优利润: 2526.50\n",
      "第 113 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 114 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 114 代的最优利润: 2526.50\n",
      "第 114 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 115 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 115 代的最优利润: 2526.50\n",
      "第 115 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 116 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 116 代的最优利润: 2526.50\n",
      "第 116 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 117 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 117 代的最优利润: 2526.50\n",
      "第 117 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 118 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 118 代的最优利润: 2526.50\n",
      "第 118 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 119 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 119 代的最优利润: 2526.50\n",
      "第 119 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 120 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 120 代的最优利润: 2526.50\n",
      "第 120 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 121 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 121 代的最优利润: 2526.50\n",
      "第 121 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 122 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 122 代的最优利润: 2526.50\n",
      "第 122 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 123 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 123 代的最优利润: 2526.50\n",
      "第 123 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 124 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 124 代的最优利润: 2526.50\n",
      "第 124 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 125 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 125 代的最优利润: 2526.50\n",
      "第 125 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 126 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 126 代的最优利润: 2526.50\n",
      "第 126 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 127 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 127 代的最优利润: 2526.50\n",
      "第 127 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 128 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 128 代的最优利润: 2526.50\n",
      "第 128 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 129 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 129 代的最优利润: 2526.50\n",
      "第 129 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 130 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 130 代的最优利润: 2526.50\n",
      "第 130 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 131 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 131 代的最优利润: 2526.50\n",
      "第 131 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 132 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 132 代的最优利润: 2526.50\n",
      "第 132 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 133 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 133 代的最优利润: 2526.50\n",
      "第 133 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 134 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 134 代的最优利润: 2526.50\n",
      "第 134 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 135 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 135 代的最优利润: 2526.50\n",
      "第 135 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 136 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 136 代的最优利润: 2526.50\n",
      "第 136 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 137 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 137 代的最优利润: 2526.50\n",
      "第 137 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 138 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 138 代的最优利润: 2526.50\n",
      "第 138 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 139 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 139 代的最优利润: 2526.50\n",
      "第 139 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 140 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 140 代的最优利润: 2526.50\n",
      "第 140 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 141 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 141 代的最优利润: 2526.50\n",
      "第 141 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 142 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 142 代的最优利润: 2526.50\n",
      "第 142 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 143 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 143 代的最优利润: 2526.50\n",
      "第 143 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 144 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 144 代的最优利润: 2526.50\n",
      "第 144 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 145 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 145 代的最优利润: 2526.50\n",
      "第 145 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 146 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 146 代的最优利润: 2526.50\n",
      "第 146 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 147 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 147 代的最优利润: 2526.50\n",
      "第 147 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 148 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 148 代的最优利润: 2526.50\n",
      "第 148 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 149 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 149 代的最优利润: 2526.50\n",
      "第 149 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 150 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 150 代的最优利润: 2526.50\n",
      "第 150 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 151 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 151 代的最优利润: 2526.50\n",
      "第 151 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 152 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 152 代的最优利润: 2526.50\n",
      "第 152 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 153 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 153 代的最优利润: 2526.50\n",
      "第 153 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 154 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 154 代的最优利润: 2526.50\n",
      "第 154 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 155 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 155 代的最优利润: 2526.50\n",
      "第 155 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 156 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 156 代的最优利润: 2526.50\n",
      "第 156 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 157 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 157 代的最优利润: 2526.50\n",
      "第 157 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 158 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 158 代的最优利润: 2526.50\n",
      "第 158 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 159 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 159 代的最优利润: 2526.50\n",
      "第 159 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 160 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 160 代的最优利润: 2526.50\n",
      "第 160 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 161 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 161 代的最优利润: 2526.50\n",
      "第 161 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 162 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 162 代的最优利润: 2526.50\n",
      "第 162 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 163 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 163 代的最优利润: 2526.50\n",
      "第 163 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 164 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 164 代的最优利润: 2526.50\n",
      "第 164 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 165 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 165 代的最优利润: 2526.50\n",
      "第 165 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 166 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 166 代的最优利润: 2526.50\n",
      "第 166 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 167 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 167 代的最优利润: 2526.50\n",
      "第 167 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 168 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 168 代的最优利润: 2526.50\n",
      "第 168 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 169 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 169 代的最优利润: 2526.50\n",
      "第 169 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 170 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 170 代的最优利润: 2526.50\n",
      "第 170 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 171 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 171 代的最优利润: 2526.50\n",
      "第 171 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 172 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 172 代的最优利润: 2526.50\n",
      "第 172 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 173 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 173 代的最优利润: 2526.50\n",
      "第 173 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 174 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 174 代的最优利润: 2526.50\n",
      "第 174 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 175 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 175 代的最优利润: 2526.50\n",
      "第 175 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 176 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 176 代的最优利润: 2526.50\n",
      "第 176 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 177 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 177 代的最优利润: 2526.50\n",
      "第 177 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 178 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 178 代的最优利润: 2526.50\n",
      "第 178 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 179 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 179 代的最优利润: 2526.50\n",
      "第 179 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 180 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 180 代的最优利润: 2526.50\n",
      "第 180 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 181 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 181 代的最优利润: 2526.50\n",
      "第 181 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 182 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 182 代的最优利润: 2526.50\n",
      "第 182 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 183 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 183 代的最优利润: 2526.50\n",
      "第 183 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 184 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 184 代的最优利润: 2526.50\n",
      "第 184 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 185 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 185 代的最优利润: 2526.50\n",
      "第 185 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 186 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 186 代的最优利润: 2526.50\n",
      "第 186 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 187 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 187 代的最优利润: 2526.50\n",
      "第 187 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 188 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 188 代的最优利润: 2526.50\n",
      "第 188 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 189 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 189 代的最优利润: 2526.50\n",
      "第 189 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 190 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 190 代的最优利润: 2526.50\n",
      "第 190 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 191 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 191 代的最优利润: 2526.50\n",
      "第 191 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 192 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 192 代的最优利润: 2526.50\n",
      "第 192 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 193 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 193 代的最优利润: 2526.50\n",
      "第 193 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 194 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 194 代的最优利润: 2526.50\n",
      "第 194 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 195 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 195 代的最优利润: 2526.50\n",
      "第 195 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 196 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 196 代的最优利润: 2526.50\n",
      "第 196 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 197 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 197 代的最优利润: 2526.50\n",
      "第 197 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 198 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 198 代的最优利润: 2526.50\n",
      "第 198 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 199 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 199 代的最优利润: 2526.50\n",
      "第 199 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 200 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 200 代的最优利润: 2526.50\n",
      "第 200 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 201 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 201 代的最优利润: 2526.50\n",
      "第 201 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 202 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 202 代的最优利润: 2526.50\n",
      "第 202 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 203 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 203 代的最优利润: 2526.50\n",
      "第 203 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 204 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 204 代的最优利润: 2526.50\n",
      "第 204 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 205 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 205 代的最优利润: 2526.50\n",
      "第 205 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 206 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 206 代的最优利润: 2526.50\n",
      "第 206 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 207 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 207 代的最优利润: 2526.50\n",
      "第 207 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 208 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 208 代的最优利润: 2526.50\n",
      "第 208 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 209 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 209 代的最优利润: 2526.50\n",
      "第 209 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 210 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 210 代的最优利润: 2526.50\n",
      "第 210 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 211 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 211 代的最优利润: 2526.50\n",
      "第 211 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 212 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 212 代的最优利润: 2526.50\n",
      "第 212 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 213 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 213 代的最优利润: 2526.50\n",
      "第 213 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 214 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 214 代的最优利润: 2526.50\n",
      "第 214 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 215 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 215 代的最优利润: 2526.50\n",
      "第 215 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 216 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 216 代的最优利润: 2526.50\n",
      "第 216 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 217 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 217 代的最优利润: 2526.50\n",
      "第 217 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 218 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 218 代的最优利润: 2526.50\n",
      "第 218 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 219 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 219 代的最优利润: 2526.50\n",
      "第 219 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 220 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 220 代的最优利润: 2526.50\n",
      "第 220 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 221 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 221 代的最优利润: 2526.50\n",
      "第 221 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 222 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 222 代的最优利润: 2526.50\n",
      "第 222 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 223 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 223 代的最优利润: 2526.50\n",
      "第 223 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 224 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 224 代的最优利润: 2526.50\n",
      "第 224 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 225 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 225 代的最优利润: 2526.50\n",
      "第 225 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 226 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 226 代的最优利润: 2526.50\n",
      "第 226 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 227 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 227 代的最优利润: 2526.50\n",
      "第 227 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 228 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 228 代的最优利润: 2526.50\n",
      "第 228 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 229 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 229 代的最优利润: 2526.50\n",
      "第 229 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 230 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 230 代的最优利润: 2526.50\n",
      "第 230 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 231 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 231 代的最优利润: 2526.50\n",
      "第 231 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 232 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 232 代的最优利润: 2526.50\n",
      "第 232 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 233 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 233 代的最优利润: 2526.50\n",
      "第 233 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 234 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 234 代的最优利润: 2526.50\n",
      "第 234 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 235 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 235 代的最优利润: 2526.50\n",
      "第 235 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 236 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 236 代的最优利润: 2526.50\n",
      "第 236 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 237 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 237 代的最优利润: 2526.50\n",
      "第 237 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 238 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 238 代的最优利润: 2526.50\n",
      "第 238 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 239 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 239 代的最优利润: 2526.50\n",
      "第 239 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 240 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 240 代的最优利润: 2526.50\n",
      "第 240 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 241 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 241 代的最优利润: 2526.50\n",
      "第 241 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 242 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 242 代的最优利润: 2526.50\n",
      "第 242 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 243 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 243 代的最优利润: 2526.50\n",
      "第 243 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 244 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 244 代的最优利润: 2526.50\n",
      "第 244 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 245 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 245 代的最优利润: 2526.50\n",
      "第 245 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 246 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 246 代的最优利润: 2526.50\n",
      "第 246 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 247 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 247 代的最优利润: 2526.50\n",
      "第 247 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 248 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 248 代的最优利润: 2526.50\n",
      "第 248 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 249 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 249 代的最优利润: 2526.50\n",
      "第 249 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 250 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 250 代的最优利润: 2526.50\n",
      "第 250 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 251 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 251 代的最优利润: 2526.50\n",
      "第 251 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 252 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 252 代的最优利润: 2526.50\n",
      "第 252 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 253 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 253 代的最优利润: 2526.50\n",
      "第 253 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 254 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 254 代的最优利润: 2526.50\n",
      "第 254 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 255 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 255 代的最优利润: 2526.50\n",
      "第 255 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 256 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 256 代的最优利润: 2526.50\n",
      "第 256 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 257 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 257 代的最优利润: 2526.50\n",
      "第 257 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 258 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 258 代的最优利润: 2526.50\n",
      "第 258 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 259 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 259 代的最优利润: 2526.50\n",
      "第 259 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 260 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 260 代的最优利润: 2526.50\n",
      "第 260 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 261 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 261 代的最优利润: 2526.50\n",
      "第 261 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 262 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 262 代的最优利润: 2526.50\n",
      "第 262 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 263 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 263 代的最优利润: 2526.50\n",
      "第 263 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 264 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 264 代的最优利润: 2526.50\n",
      "第 264 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 265 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 265 代的最优利润: 2526.50\n",
      "第 265 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 266 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 266 代的最优利润: 2526.50\n",
      "第 266 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 267 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 267 代的最优利润: 2526.50\n",
      "第 267 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 268 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 268 代的最优利润: 2526.50\n",
      "第 268 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 269 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 269 代的最优利润: 2526.50\n",
      "第 269 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 270 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 270 代的最优利润: 2526.50\n",
      "第 270 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 271 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 271 代的最优利润: 2526.50\n",
      "第 271 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 272 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 272 代的最优利润: 2526.50\n",
      "第 272 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 273 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 273 代的最优利润: 2526.50\n",
      "第 273 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 274 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 274 代的最优利润: 2526.50\n",
      "第 274 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 275 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 275 代的最优利润: 2526.50\n",
      "第 275 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 276 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 276 代的最优利润: 2526.50\n",
      "第 276 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 277 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 277 代的最优利润: 2526.50\n",
      "第 277 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 278 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 278 代的最优利润: 2526.50\n",
      "第 278 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 279 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 279 代的最优利润: 2526.50\n",
      "第 279 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 280 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 280 代的最优利润: 2526.50\n",
      "第 280 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 281 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 281 代的最优利润: 2526.50\n",
      "第 281 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 282 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 282 代的最优利润: 2526.50\n",
      "第 282 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 283 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 283 代的最优利润: 2526.50\n",
      "第 283 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 284 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 284 代的最优利润: 2526.50\n",
      "第 284 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 285 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 285 代的最优利润: 2526.50\n",
      "第 285 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 286 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 286 代的最优利润: 2526.50\n",
      "第 286 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 287 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 287 代的最优利润: 2526.50\n",
      "第 287 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 288 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 288 代的最优利润: 2526.50\n",
      "第 288 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 289 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 289 代的最优利润: 2526.50\n",
      "第 289 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 290 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 290 代的最优利润: 2526.50\n",
      "第 290 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 291 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 291 代的最优利润: 2526.50\n",
      "第 291 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 292 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 292 代的最优利润: 2526.50\n",
      "第 292 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 293 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 293 代的最优利润: 2526.50\n",
      "第 293 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 294 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 294 代的最优利润: 2526.50\n",
      "第 294 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 295 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 295 代的最优利润: 2526.50\n",
      "第 295 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 296 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 296 代的最优利润: 2526.50\n",
      "第 296 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 297 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 297 代的最优利润: 2526.50\n",
      "第 297 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 298 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 298 代的最优利润: 2526.50\n",
      "第 298 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 299 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 299 代的最优利润: 2526.50\n",
      "第 299 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 300 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 300 代的最优利润: 2526.50\n",
      "第 300 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 301 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 301 代的最优利润: 2526.50\n",
      "第 301 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 302 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 302 代的最优利润: 2526.50\n",
      "第 302 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 303 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 303 代的最优利润: 2526.50\n",
      "第 303 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 304 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 304 代的最优利润: 2526.50\n",
      "第 304 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 305 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 305 代的最优利润: 2526.50\n",
      "第 305 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 306 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 306 代的最优利润: 2526.50\n",
      "第 306 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 307 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 307 代的最优利润: 2526.50\n",
      "第 307 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 308 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 308 代的最优利润: 2526.50\n",
      "第 308 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 309 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 309 代的最优利润: 2526.50\n",
      "第 309 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 310 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 310 代的最优利润: 2526.50\n",
      "第 310 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 311 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 311 代的最优利润: 2526.50\n",
      "第 311 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 312 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 312 代的最优利润: 2526.50\n",
      "第 312 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 313 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 313 代的最优利润: 2526.50\n",
      "第 313 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 314 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 314 代的最优利润: 2526.50\n",
      "第 314 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 315 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 315 代的最优利润: 2526.50\n",
      "第 315 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 316 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 316 代的最优利润: 2526.50\n",
      "第 316 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 317 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 317 代的最优利润: 2526.50\n",
      "第 317 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 318 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 318 代的最优利润: 2526.50\n",
      "第 318 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 319 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 319 代的最优利润: 2526.50\n",
      "第 319 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 320 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 320 代的最优利润: 2526.50\n",
      "第 320 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 321 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 321 代的最优利润: 2526.50\n",
      "第 321 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 322 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 322 代的最优利润: 2526.50\n",
      "第 322 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 323 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 323 代的最优利润: 2526.50\n",
      "第 323 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 324 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 324 代的最优利润: 2526.50\n",
      "第 324 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 325 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 325 代的最优利润: 2526.50\n",
      "第 325 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 326 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 326 代的最优利润: 2526.50\n",
      "第 326 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 327 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 327 代的最优利润: 2526.50\n",
      "第 327 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 328 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 328 代的最优利润: 2526.50\n",
      "第 328 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 329 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 329 代的最优利润: 2526.50\n",
      "第 329 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 330 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 330 代的最优利润: 2526.50\n",
      "第 330 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 331 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 331 代的最优利润: 2526.50\n",
      "第 331 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 332 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 332 代的最优利润: 2526.50\n",
      "第 332 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 333 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 333 代的最优利润: 2526.50\n",
      "第 333 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 334 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 334 代的最优利润: 2526.50\n",
      "第 334 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 335 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 335 代的最优利润: 2526.50\n",
      "第 335 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 336 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 336 代的最优利润: 2526.50\n",
      "第 336 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 337 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 337 代的最优利润: 2526.50\n",
      "第 337 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 338 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 338 代的最优利润: 2526.50\n",
      "第 338 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 339 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 339 代的最优利润: 2526.50\n",
      "第 339 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 340 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 340 代的最优利润: 2526.50\n",
      "第 340 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 341 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 341 代的最优利润: 2526.50\n",
      "第 341 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 342 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 342 代的最优利润: 2526.50\n",
      "第 342 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 343 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 343 代的最优利润: 2526.50\n",
      "第 343 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 344 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 344 代的最优利润: 2526.50\n",
      "第 344 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 345 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 345 代的最优利润: 2526.50\n",
      "第 345 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 346 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 346 代的最优利润: 2526.50\n",
      "第 346 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 347 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 347 代的最优利润: 2526.50\n",
      "第 347 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 348 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 348 代的最优利润: 2526.50\n",
      "第 348 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 349 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 349 代的最优利润: 2526.50\n",
      "第 349 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 350 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 350 代的最优利润: 2526.50\n",
      "第 350 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 351 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 351 代的最优利润: 2526.50\n",
      "第 351 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 352 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 352 代的最优利润: 2526.50\n",
      "第 352 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 353 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 353 代的最优利润: 2526.50\n",
      "第 353 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 354 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 354 代的最优利润: 2526.50\n",
      "第 354 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 355 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 355 代的最优利润: 2526.50\n",
      "第 355 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 356 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 356 代的最优利润: 2526.50\n",
      "第 356 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 357 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 357 代的最优利润: 2526.50\n",
      "第 357 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 358 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 358 代的最优利润: 2526.50\n",
      "第 358 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 359 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 359 代的最优利润: 2526.50\n",
      "第 359 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 360 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 360 代的最优利润: 2526.50\n",
      "第 360 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 361 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 361 代的最优利润: 2526.50\n",
      "第 361 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 362 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 362 代的最优利润: 2526.50\n",
      "第 362 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 363 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 363 代的最优利润: 2526.50\n",
      "第 363 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 364 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 364 代的最优利润: 2526.50\n",
      "第 364 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 365 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 365 代的最优利润: 2526.50\n",
      "第 365 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 366 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 366 代的最优利润: 2526.50\n",
      "第 366 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 367 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 367 代的最优利润: 2526.50\n",
      "第 367 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 368 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 368 代的最优利润: 2526.50\n",
      "第 368 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 369 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 369 代的最优利润: 2526.50\n",
      "第 369 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 370 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 370 代的最优利润: 2526.50\n",
      "第 370 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 371 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 371 代的最优利润: 2526.50\n",
      "第 371 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 372 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 372 代的最优利润: 2526.50\n",
      "第 372 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 373 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 373 代的最优利润: 2526.50\n",
      "第 373 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 374 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 374 代的最优利润: 2526.50\n",
      "第 374 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 375 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 375 代的最优利润: 2526.50\n",
      "第 375 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 376 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 376 代的最优利润: 2526.50\n",
      "第 376 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 377 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 377 代的最优利润: 2526.50\n",
      "第 377 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 378 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 378 代的最优利润: 2526.50\n",
      "第 378 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 379 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 379 代的最优利润: 2526.50\n",
      "第 379 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 380 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 380 代的最优利润: 2526.50\n",
      "第 380 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 381 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 381 代的最优利润: 2526.50\n",
      "第 381 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 382 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 382 代的最优利润: 2526.50\n",
      "第 382 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 383 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 383 代的最优利润: 2526.50\n",
      "第 383 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 384 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 384 代的最优利润: 2526.50\n",
      "第 384 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 385 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 385 代的最优利润: 2526.50\n",
      "第 385 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 386 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 386 代的最优利润: 2526.50\n",
      "第 386 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 387 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 387 代的最优利润: 2526.50\n",
      "第 387 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 388 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 388 代的最优利润: 2526.50\n",
      "第 388 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 389 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 389 代的最优利润: 2526.50\n",
      "第 389 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 390 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 390 代的最优利润: 2526.50\n",
      "第 390 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 391 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 391 代的最优利润: 2526.50\n",
      "第 391 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 392 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 392 代的最优利润: 2526.50\n",
      "第 392 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 393 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 393 代的最优利润: 2526.50\n",
      "第 393 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 394 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 394 代的最优利润: 2526.50\n",
      "第 394 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 395 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 395 代的最优利润: 2526.50\n",
      "第 395 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 396 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 396 代的最优利润: 2526.50\n",
      "第 396 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 397 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 397 代的最优利润: 2526.50\n",
      "第 397 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 398 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 398 代的最优利润: 2526.50\n",
      "第 398 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 399 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 399 代的最优利润: 2526.50\n",
      "第 399 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 400 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 400 代的最优利润: 2526.50\n",
      "第 400 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 401 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 401 代的最优利润: 2526.50\n",
      "第 401 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 402 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 402 代的最优利润: 2526.50\n",
      "第 402 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 403 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 403 代的最优利润: 2526.50\n",
      "第 403 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 404 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 404 代的最优利润: 2526.50\n",
      "第 404 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 405 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 405 代的最优利润: 2526.50\n",
      "第 405 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 406 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 406 代的最优利润: 2526.50\n",
      "第 406 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 407 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 407 代的最优利润: 2526.50\n",
      "第 407 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 408 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 408 代的最优利润: 2526.50\n",
      "第 408 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 409 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 409 代的最优利润: 2526.50\n",
      "第 409 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 410 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 410 代的最优利润: 2526.50\n",
      "第 410 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 411 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 411 代的最优利润: 2526.50\n",
      "第 411 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 412 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 412 代的最优利润: 2526.50\n",
      "第 412 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 413 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 413 代的最优利润: 2526.50\n",
      "第 413 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 414 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 414 代的最优利润: 2526.50\n",
      "第 414 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 415 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 415 代的最优利润: 2526.50\n",
      "第 415 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 416 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 416 代的最优利润: 2526.50\n",
      "第 416 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 417 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 417 代的最优利润: 2526.50\n",
      "第 417 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 418 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 418 代的最优利润: 2526.50\n",
      "第 418 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 419 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 419 代的最优利润: 2526.50\n",
      "第 419 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 420 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 420 代的最优利润: 2526.50\n",
      "第 420 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 421 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 421 代的最优利润: 2526.50\n",
      "第 421 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 422 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 422 代的最优利润: 2526.50\n",
      "第 422 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 423 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 423 代的最优利润: 2526.50\n",
      "第 423 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 424 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 424 代的最优利润: 2526.50\n",
      "第 424 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 425 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 425 代的最优利润: 2526.50\n",
      "第 425 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 426 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 426 代的最优利润: 2526.50\n",
      "第 426 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 427 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 427 代的最优利润: 2526.50\n",
      "第 427 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 428 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 428 代的最优利润: 2526.50\n",
      "第 428 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 429 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 429 代的最优利润: 2526.50\n",
      "第 429 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 430 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 430 代的最优利润: 2526.50\n",
      "第 430 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 431 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 431 代的最优利润: 2526.50\n",
      "第 431 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 432 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 432 代的最优利润: 2526.50\n",
      "第 432 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 433 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 433 代的最优利润: 2526.50\n",
      "第 433 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 434 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 434 代的最优利润: 2526.50\n",
      "第 434 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 435 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 435 代的最优利润: 2526.50\n",
      "第 435 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 436 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 436 代的最优利润: 2526.50\n",
      "第 436 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 437 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 437 代的最优利润: 2526.50\n",
      "第 437 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 438 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 438 代的最优利润: 2526.50\n",
      "第 438 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 439 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 439 代的最优利润: 2526.50\n",
      "第 439 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 440 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 440 代的最优利润: 2526.50\n",
      "第 440 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 441 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 441 代的最优利润: 2526.50\n",
      "第 441 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 442 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 442 代的最优利润: 2526.50\n",
      "第 442 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 443 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 443 代的最优利润: 2526.50\n",
      "第 443 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 444 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 444 代的最优利润: 2526.50\n",
      "第 444 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 445 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 445 代的最优利润: 2526.50\n",
      "第 445 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 446 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 446 代的最优利润: 2526.50\n",
      "第 446 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 447 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 447 代的最优利润: 2526.50\n",
      "第 447 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 448 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 448 代的最优利润: 2526.50\n",
      "第 448 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 449 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 449 代的最优利润: 2526.50\n",
      "第 449 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 450 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 450 代的最优利润: 2526.50\n",
      "第 450 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 451 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 451 代的最优利润: 2526.50\n",
      "第 451 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 452 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 452 代的最优利润: 2526.50\n",
      "第 452 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 453 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 453 代的最优利润: 2526.50\n",
      "第 453 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 454 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 454 代的最优利润: 2526.50\n",
      "第 454 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 455 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 455 代的最优利润: 2526.50\n",
      "第 455 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 456 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 456 代的最优利润: 2526.50\n",
      "第 456 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 457 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 457 代的最优利润: 2526.50\n",
      "第 457 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 458 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 458 代的最优利润: 2526.50\n",
      "第 458 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 459 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 459 代的最优利润: 2526.50\n",
      "第 459 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 460 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 460 代的最优利润: 2526.50\n",
      "第 460 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 461 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 461 代的最优利润: 2526.50\n",
      "第 461 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 462 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 462 代的最优利润: 2526.50\n",
      "第 462 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 463 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 463 代的最优利润: 2526.50\n",
      "第 463 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 464 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 464 代的最优利润: 2526.50\n",
      "第 464 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 465 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 465 代的最优利润: 2526.50\n",
      "第 465 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 466 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 466 代的最优利润: 2526.50\n",
      "第 466 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 467 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 467 代的最优利润: 2526.50\n",
      "第 467 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 468 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 468 代的最优利润: 2526.50\n",
      "第 468 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 469 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 469 代的最优利润: 2526.50\n",
      "第 469 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 470 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 470 代的最优利润: 2526.50\n",
      "第 470 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 471 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 471 代的最优利润: 2526.50\n",
      "第 471 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 472 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 472 代的最优利润: 2526.50\n",
      "第 472 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 473 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 473 代的最优利润: 2526.50\n",
      "第 473 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 474 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 474 代的最优利润: 2526.50\n",
      "第 474 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 475 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 475 代的最优利润: 2526.50\n",
      "第 475 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 476 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 476 代的最优利润: 2526.50\n",
      "第 476 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 477 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 477 代的最优利润: 2526.50\n",
      "第 477 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 478 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 478 代的最优利润: 2526.50\n",
      "第 478 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 479 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 479 代的最优利润: 2526.50\n",
      "第 479 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 480 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 480 代的最优利润: 2526.50\n",
      "第 480 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 481 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 481 代的最优利润: 2526.50\n",
      "第 481 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 482 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 482 代的最优利润: 2526.50\n",
      "第 482 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 483 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 483 代的最优利润: 2526.50\n",
      "第 483 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 484 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 484 代的最优利润: 2526.50\n",
      "第 484 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 485 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 485 代的最优利润: 2526.50\n",
      "第 485 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 486 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 486 代的最优利润: 2526.50\n",
      "第 486 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 487 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 487 代的最优利润: 2526.50\n",
      "第 487 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 488 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 488 代的最优利润: 2526.50\n",
      "第 488 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 489 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 489 代的最优利润: 2526.50\n",
      "第 489 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 490 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 490 代的最优利润: 2526.50\n",
      "第 490 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 491 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 491 代的最优利润: 2526.50\n",
      "第 491 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第 492 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 492 代的最优利润: 2526.50\n",
      "第 492 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 493 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 493 代的最优利润: 2526.50\n",
      "第 493 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 494 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 494 代的最优利润: 2526.50\n",
      "第 494 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 495 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 495 代的最优利润: 2526.50\n",
      "第 495 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 496 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 496 代的最优利润: 2526.50\n",
      "第 496 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 497 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 497 代的最优利润: 2526.50\n",
      "第 497 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 498 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 498 代的最优利润: 2526.50\n",
      "第 498 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 499 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 499 代的最优利润: 2526.50\n",
      "第 499 代的最优销量: 77.12\n",
      "--------------------------------------------------\n",
      "第 500 代的最优策略 (零配件检测决策 x_ij): [[1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 0 0 1 0]]\n",
      "成品检测决策 x_f: 1\n",
      "拆解决策 x_d: 1\n",
      "第 500 代的最优利润: 2526.50\n",
      "第 500 代的最优销量: 77.12\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 参数定义\n",
    "p = 200  # 成品的市场售价\n",
    "m = 2  # 工序数\n",
    "n = 8  # 零配件数\n",
    "N = 100  # 假设的生产数量\n",
    "\n",
    "# 零配件次品率、成本、检测成本\n",
    "q_ij = [0.1] * n  # 每个零配件的次品率\n",
    "c_ij = [2, 8, 12, 2, 8, 12, 8, 12]  # 零配件的成本\n",
    "d_ij = [1, 1, 2, 1, 1, 2, 1, 2]  # 零配件检测成本\n",
    "\n",
    "# 成品次品率、装配成本、检测成本\n",
    "p_f = 0.1  # 成品次品率\n",
    "c_f = 8  # 成品装配成本\n",
    "d_f = 6  # 成品检测成本\n",
    "r = 40  # 成品调换损失\n",
    "d_c = 10  # 成品拆解成本\n",
    "\n",
    "# 决策变量生成函数（遗传算法种群初始化）\n",
    "def generate_population(size):\n",
    "    population = []\n",
    "    for _ in range(size):\n",
    "        # 每个个体包括 m*n 个零配件的检测决策，1个成品检测决策，1个拆解决策\n",
    "        x_ij = np.random.randint(0, 2, size=(m, n))  # 零配件检测决策\n",
    "        x_f = np.random.randint(0, 2)  # 成品检测决策\n",
    "        x_d = np.random.randint(0, 2)  # 拆解决策\n",
    "        population.append((x_ij, x_f, x_d))\n",
    "    return population\n",
    "\n",
    "# 计算利润和销量的函数\n",
    "def compute_profit_and_sales(x_ij, x_f, x_d):\n",
    "    total_profit = 0\n",
    "    total_sales = 0\n",
    "    \n",
    "    # 零配件阶段利润和次品率计算\n",
    "    defect_rate = 1  # 初始次品率为 1\n",
    "    for i in range(m):\n",
    "        for j in range(n):\n",
    "            if x_ij[i][j] == 0:\n",
    "                total_profit -= c_ij[j]  # 不检测，直接产生成本\n",
    "            else:\n",
    "                # 检测后减少次品率\n",
    "                total_profit += (1 - q_ij[j]) * (p - c_ij[j] - d_ij[j])  \n",
    "                defect_rate *= (1 - q_ij[j])\n",
    "\n",
    "    # 成品阶段利润计算\n",
    "    if x_f == 0:\n",
    "        total_profit += (1 - p_f) * p - c_f - r  # 不检测\n",
    "        defect_rate *= p_f\n",
    "    else:\n",
    "        total_profit += (1 - p_f) * (p - d_f) - c_f  # 检测后利润\n",
    "        defect_rate *= (1 - p_f)\n",
    "\n",
    "    # 计算合格产品数量（销量）\n",
    "    qualified_products = (1 - defect_rate) * N\n",
    "    total_sales = qualified_products\n",
    "\n",
    "    # 拆解阶段利润计算\n",
    "    if x_d == 0:\n",
    "        total_profit -= d_c  # 不拆解\n",
    "    else:\n",
    "        total_profit += sum([p_j * (p - c_ij[j]) for j, p_j in enumerate(q_ij)])  # 拆解后回收零配件\n",
    "\n",
    "    return total_profit, total_sales\n",
    "def genetic_algorithm_optimization(pop_size, generations, simulations, mutation_rate):\n",
    "    # 初始化种群\n",
    "    population = generate_population(pop_size)\n",
    "    avg_sales_per_generation = []\n",
    "    avg_profits_per_generation = []\n",
    "    best_strategies = []\n",
    "    best_sales_per_generation = []\n",
    "    best_profits_per_generation = []\n",
    "\n",
    "    for generation in range(generations):\n",
    "        total_sales_all_strategies = []\n",
    "        total_profits_all_strategies = []\n",
    "        strategy_info = []\n",
    "        \n",
    "        # 计算每个个体的平均期望销量和平均期望利润\n",
    "        for individual in population:\n",
    "            x_ij, x_f, x_d = individual\n",
    "            sales_list = []\n",
    "            profit_list = []\n",
    "            for _ in range(simulations):  # 多次模拟\n",
    "                profit, sales = compute_profit_and_sales(x_ij, x_f, x_d)\n",
    "                sales_list.append(sales)\n",
    "                profit_list.append(profit)\n",
    "\n",
    "            avg_sales = np.mean(sales_list)  # 计算平均期望销量\n",
    "            avg_profit = np.mean(profit_list)  # 计算平均期望利润\n",
    "            total_sales_all_strategies.append(avg_sales)\n",
    "            total_profits_all_strategies.append(avg_profit)\n",
    "            strategy_info.append((individual, avg_profit, avg_sales))\n",
    "\n",
    "        # 找到当前代次的最佳策略\n",
    "        best_individual, best_profit, best_sales = max(strategy_info, key=lambda x: x[1])\n",
    "        best_strategies.append(best_individual)\n",
    "        best_profits_per_generation.append(best_profit)\n",
    "        best_sales_per_generation.append(best_sales)\n",
    "\n",
    "        # 输出每代次的最优策略、最优利润和最优销量\n",
    "        print(f\"第 {generation + 1} 代的最优策略 (零配件检测决策 x_ij): {best_individual[0]}\")\n",
    "        print(f\"成品检测决策 x_f: {best_individual[1]}\")\n",
    "        print(f\"拆解决策 x_d: {best_individual[2]}\")\n",
    "        print(f\"第 {generation + 1} 代的最优利润: {best_profit:.2f}\")\n",
    "        print(f\"第 {generation + 1} 代的最优销量: {best_sales:.2f}\")\n",
    "        print('-' * 50)\n",
    "\n",
    "        # 记录每一代次的平均期望销量和平均期望利润\n",
    "        avg_sales_per_generation.append(np.mean(total_sales_all_strategies))\n",
    "        avg_profits_per_generation.append(np.mean(total_profits_all_strategies))\n",
    "\n",
    "        # 选择过程\n",
    "        sorted_population = sorted(list(zip(population, total_profits_all_strategies)), key=lambda x: x[1], reverse=True)\n",
    "        parent_population = [x[0] for x in sorted_population[:pop_size // 2]]\n",
    "\n",
    "        # 交叉生成后代\n",
    "        new_population = []\n",
    "        for _ in range(pop_size // 2):\n",
    "            parent1, parent2 = np.random.choice(len(parent_population), size=2, replace=False)\n",
    "            parent1 = parent_population[parent1]\n",
    "            parent2 = parent_population[parent2]\n",
    "\n",
    "            # 交叉点的选择\n",
    "            crossover_point = np.random.randint(1, n)\n",
    "            offspring1_x_ij = np.vstack((parent1[0][:crossover_point], parent2[0][crossover_point:]))\n",
    "            offspring2_x_ij = np.vstack((parent2[0][:crossover_point], parent1[0][crossover_point:]))\n",
    "\n",
    "            offspring1 = (offspring1_x_ij, parent1[1], parent1[2])\n",
    "            offspring2 = (offspring2_x_ij, parent2[1], parent2[2])\n",
    "            new_population.extend([offspring1, offspring2])\n",
    "\n",
    "        # 变异过程\n",
    "        for i in range(len(new_population)):\n",
    "            if np.random.rand() < mutation_rate:\n",
    "                mutation_idx = np.random.randint(0, n)\n",
    "                new_population[i][0][0][mutation_idx] = 1 - new_population[i][0][0][mutation_idx]  # 变异\n",
    "\n",
    "        population = new_population\n",
    "\n",
    "    return avg_sales_per_generation, avg_profits_per_generation, best_sales_per_generation, best_profits_per_generation\n",
    "\n",
    "# 执行遗传算法\n",
    "simulations = 100  # 蒙特卡洛模拟次数\n",
    "avg_sales_per_generation, avg_profits_per_generation, best_sales_per_generation, best_profits_per_generation = genetic_algorithm_optimization(pop_size=10, generations=500, simulations=simulations, mutation_rate=0.1)\n",
    "\n",
    "# 可视化平均期望利润结果\n",
    "plt.plot(range(1, 501), avg_profits_per_generation)\n",
    "plt.title(\"Average Profit per Generation\")\n",
    "plt.xlabel(\"Generation\")\n",
    "plt.ylabel(\"Average Profit\")\n",
    "plt.show()\n",
    "\n",
    "# 可视化平均期望销量结果\n",
    "plt.plot(range(1, 501), avg_sales_per_generation)\n",
    "plt.title(\"Average Sales per Generation\")\n",
    "plt.xlabel(\"Generation\")\n",
    "plt.ylabel(\"Average Sales\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "731c92ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# 参数定义\n",
    "p = 200  # 成品的市场售价\n",
    "m = 2  # 工序数\n",
    "n = 8  # 零配件数\n",
    "N = 100  # 假设的生产数量\n",
    "\n",
    "# 零配件次品率、成本、检测成本\n",
    "q_ij = [0.1] * n  # 每个零配件的次品率\n",
    "c_ij = [2, 8, 12, 2, 8, 12, 8, 12]  # 零配件的成本\n",
    "d_ij = [1, 1, 2, 1, 1, 2, 1, 2]  # 零配件检测成本\n",
    "\n",
    "# 成品次品率、装配成本、检测成本\n",
    "p_f = 0.1  # 成品次品率\n",
    "c_f = 8  # 成品装配成本\n",
    "d_f = 6  # 成品检测成本\n",
    "r = 40  # 成品调换损失\n",
    "d_c = 10  # 成品拆解成本\n",
    "\n",
    "# 决策变量生成函数（遗传算法种群初始化）\n",
    "def generate_population(size):\n",
    "    population = []\n",
    "    for _ in range(size):\n",
    "        # 每个个体包括 m*n 个零配件的检测决策，1个成品检测决策，1个拆解决策\n",
    "        x_ij = np.random.randint(0, 2, size=(m, n))  # 零配件检测决策\n",
    "        x_f = np.random.randint(0, 2)  # 成品检测决策\n",
    "        x_d = np.random.randint(0, 2)  # 拆解决策\n",
    "        population.append((x_ij, x_f, x_d))\n",
    "    return population\n",
    "\n",
    "# 计算利润和销量的函数，增加了蒙特卡洛模拟\n",
    "def compute_profit_and_sales(x_ij, x_f, x_d, simulations):\n",
    "    total_profits = []\n",
    "    total_sales = []\n",
    "    \n",
    "    for _ in range(simulations):  # 进行多次模拟\n",
    "        total_profit = 0\n",
    "        total_sales_for_simulation = 0\n",
    "        defect_rate = 1  # 初始次品率为 1\n",
    "        \n",
    "        # 零配件阶段利润和次品率计算\n",
    "        for i in range(m):\n",
    "            for j in range(n):\n",
    "                if x_ij[i][j] == 0:\n",
    "                    total_profit -= c_ij[j]  # 不检测，直接产生成本\n",
    "                else:\n",
    "                    # 检测后减少次品率\n",
    "                    total_profit += (1 - q_ij[j]) * (p - c_ij[j] - d_ij[j])\n",
    "                    defect_rate *= (1 - q_ij[j])\n",
    "\n",
    "        # 成品阶段利润计算\n",
    "        if x_f == 0:\n",
    "            total_profit += (1 - p_f) * p - c_f - r  # 不检测\n",
    "            defect_rate *= p_f\n",
    "        else:\n",
    "            total_profit += (1 - p_f) * (p - d_f) - c_f  # 检测后利润\n",
    "            defect_rate *= (1 - p_f)\n",
    "\n",
    "        # 计算合格产品数量（销量）\n",
    "        qualified_products = (1 - defect_rate) * N\n",
    "        total_sales_for_simulation = qualified_products\n",
    "\n",
    "        # 拆解阶段利润计算\n",
    "        if x_d == 0:\n",
    "            total_profit -= d_c  # 不拆解\n",
    "        else:\n",
    "            total_profit += sum([p_j * (p - c_ij[j]) for j, p_j in enumerate(q_ij)])  # 拆解后回收零配件\n",
    "\n",
    "        total_profits.append(total_profit)\n",
    "        total_sales.append(total_sales_for_simulation)\n",
    "\n",
    "    # 返回多次模拟的平均利润和平均销量\n",
    "    avg_profit = np.mean(total_profits)\n",
    "    avg_sales = np.mean(total_sales)\n",
    "    return avg_profit, avg_sales\n",
    "\n",
    "# 遗传算法主流程\n",
    "def genetic_algorithm_optimization(pop_size, generations, simulations, mutation_rate):\n",
    "    # 初始化种群\n",
    "    population = generate_population(pop_size)\n",
    "    best_strategy = None\n",
    "    best_profit = -np.inf\n",
    "    \n",
    "    for generation in range(generations):\n",
    "        profits = []\n",
    "        \n",
    "        # 计算每个个体的平均利润和销量（蒙特卡洛模拟）\n",
    "        for individual in population:\n",
    "            x_ij, x_f, x_d = individual\n",
    "            profit, sales = compute_profit_and_sales(x_ij, x_f, x_d, simulations)\n",
    "            profits.append((individual, profit))\n",
    "        \n",
    "        # 找到当前种群中利润最高的个体\n",
    "        current_best_individual, current_best_profit = max(profits, key=lambda x: x[1])\n",
    "\n",
    "        # 更新最优个体\n",
    "        if current_best_profit > best_profit:\n",
    "            best_strategy = current_best_individual\n",
    "            best_profit = current_best_profit\n",
    "\n",
    "        # 选择过程\n",
    "        sorted_population = sorted(profits, key=lambda x: x[1], reverse=True)\n",
    "        parent_population = [x[0] for x in sorted_population[:pop_size // 2]]\n",
    "\n",
    "        # 交叉生成后代\n",
    "        new_population = []\n",
    "        for _ in range(pop_size // 2):\n",
    "            parent1, parent2 = np.random.choice(len(parent_population), size=2, replace=False)\n",
    "            parent1 = parent_population[parent1]\n",
    "            parent2 = parent_population[parent2]\n",
    "            \n",
    "            # 交叉点的选择\n",
    "            crossover_point = np.random.randint(1, n)\n",
    "            offspring1_x_ij = np.vstack((parent1[0][:crossover_point], parent2[0][crossover_point:]))\n",
    "            offspring2_x_ij = np.vstack((parent2[0][:crossover_point], parent1[0][crossover_point:]))\n",
    "\n",
    "            offspring1 = (offspring1_x_ij, parent1[1], parent1[2])\n",
    "            offspring2 = (offspring2_x_ij, parent2[1], parent2[2])\n",
    "            new_population.extend([offspring1, offspring2])\n",
    "\n",
    "        # 变异过程\n",
    "        for i in range(len(new_population)):\n",
    "            if np.random.rand() < mutation_rate:\n",
    "                mutation_idx = np.random.randint(0, n)\n",
    "                new_population[i][0][0][mutation_idx] = 1 - new_population[i][0][0][mutation_idx]  # 变异\n",
    "\n",
    "        population = new_population\n",
    "\n",
    "    return best_strategy, best_profit\n",
    "\n",
    "# 遗传代数从10到500，每隔10次输出结果\n",
    "generations_range = range(10, 501, 5)\n",
    "best_profits = []\n",
    "best_strategies = []\n",
    "simulations = 100  # 蒙特卡洛模拟的次数\n",
    "\n",
    "for gens in generations_range:\n",
    "    best_strategy, best_profit = genetic_algorithm_optimization(pop_size=10, generations=gens, simulations=simulations, mutation_rate=0.1)\n",
    "    best_profits.append(best_profit)\n",
    "    best_strategies.append(best_strategy)\n",
    "\n",
    "# 可视化不同遗传代数下的最优利润变化\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.plot(generations_range, best_profits, color='red', linewidth=2)\n",
    "plt.title(\"Best Profit vs Generations\", fontsize=16)\n",
    "plt.xlabel(\"Generations\", fontsize=14)\n",
    "plt.ylabel(\"Best Profit\", fontsize=14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 保存结果到Excel文件\n",
    "results = []\n",
    "for gens, profit, strategy in zip(generations_range, best_profits, best_strategies):\n",
    "    x_ij, x_f, x_d = strategy\n",
    "    results.append({\n",
    "        'Generations': gens,\n",
    "        'Best Profit': profit,\n",
    "        'Best Strategy (x_ij)': str(x_ij),\n",
    "        'Final Product Inspection (x_f)': x_f,\n",
    "        'Decomposition Decision (x_d)': x_d\n",
    "    })\n",
    "\n",
    "# Convert results to a DataFrame\n",
    "df = pd.DataFrame(results)\n",
    "\n",
    "# Save to Excel using `with open` via `ExcelWriter`\n",
    "with pd.ExcelWriter('genetic_algorithm_results2.xlsx') as writer:\n",
    "    df.to_excel(writer, index=False)\n",
    "\n",
    "print(\"Results saved in genetic_algorithm_results2.xlsx\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d67ab9fb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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